Senior MLOps Engineer – Edge

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🔥 0 minutes ago

🌐 Spain, United Kingdom – Remote

🇪🇸 Spain – Remote 🇬🇧 United Kingdom – Remote

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

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Hudl

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

⚽ Sports

🤝 B2B

💰 $120M Venture Round on 2020-05

Sports • B2B

Hudl is an industry leader in performance analysis software and hardware, empowering more than 200K teams in 40+ sports worldwide to achieve their goals with best-in-class video and data technology. A complete suite of video and data products ensures coaches have the insights they need and athletes get the shot they deserve.

📋 Description

• Design, develop, and maintain scalable edge delivery systems for deploying machine learning models to fleets of devices. • Own the model compilation platform that converts trained models into optimized, hardware-specific inference engines. • Manage TensorRT compilation, FP16/INT8 precision trade-offs, calibration, and engine validation. • Collaborate with Data Scientists, Embedded Engineers, and Product Managers to integrate complex features. • Implement infrastructure for silent candidate-model testing on production devices. • Build telemetry pipelines to monitor model drift, thermal impact, and inference latency. • Develop resilient update mechanisms for low-bandwidth environments and limited-storage devices. • Ensure devices recover gracefully from network failures. • Establish best practices in Python tooling, Infrastructure-as-Code, and CI/CD. • Mentor and guide the team toward robust, automated systems.

🎯 Requirements

• Production MLOps expertise building and operating production model-deployment pipelines • Deep experience with CI/CD, Docker, and Linux systems • Hands-on experience compiling and optimizing machine learning models for embedded hardware • Understanding of precision, quantization, and inference-engine validation at scale • Ability to collaborate with researchers and low-level embedded engineers • Ability to design architectures that handle failures gracefully • Understanding of deploying to 10,000 heterogeneous devices • Knowledge of canary releases and safe rollbacks • Initiative and willingness to fill gaps and solve problems • NVIDIA edge ecosystem experience, Jetson Orin, DeepStream SDK, and TensorRT are advantageous • Familiarity with video pipelines, GStreamer, or ffmpeg is advantageous • Experience with AWS IoT Greengrass, Balena, or custom OTA/fleet-management solutions is advantageous • Interest in sports technology, video analytics, or performance metrics is advantageous

🏖️ Benefits

• Flexible vacation time • Company-wide holidays • Timeout (meeting-free) days • Remote work options • Professional development resources and opportunities • Tech stack and hardware for working in the office or remotely • Medical benefits, depending on location • Retirement benefits, depending on location • Employee Assistance Program • Employee resource groups • Mental health support resources • Open, honest culture and autonomy